A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data & Knowledge Engineering
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
On schema matching with opaque column names and data values
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Instance-based attribute identification in database integration
The VLDB Journal — The International Journal on Very Large Data Bases
Schema Matching Using Duplicates
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Instance-based schema matching for web databases by domain-specific query probing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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The process of schema matching lies at the heart of database applications related to data integration. Many instance-based solutions to the schema matching problem have been proposed. These approaches focus on analyzing the values of attributes especially within the application domain. The approach presented in this paper is a two-step domain-independent schema matching technique. The technique first measures shared information between pair-wise attributes using the concept of mutual information. Next, a graph representation with weighted links is constructed for each input schema. At this stage, schema matching switches to a weighted graph matching problem. At this stage, a graduated assignment algorithm is applied to find the correspondence of vertices between graphs. We perform experiments using two real-world data sets in different application domains to roughly evaluate the performance of this schema matching technique in terms of precision, recall and running time.